Cap11: Why model?
UnB
Part I conceitual, estimadores não paramétricos
Part II dados “reais”, estimadores paramétricos
We define nonparametric estimators: those that produce estimates from the data without any a priori restrictions on the conditional mean function: Part I
Parametric estimation and other approaches to borrow information are our only hope when data are unable to speak for themselves.
16 individuals infected with HIV randomly sampled from a larger target population.
Each individual receives a certain level of antiretroviral therapy. At the end CD4 cell count, in cells/mm3 is measured: We wish to consistently estimate the mean of cell counts for individuals with level A=a.
\(\hat{E}=[Y|A=a]\) é um estimador consistente.
Min. 1st Qu. Median Mean 3rd Qu. Max.
10.0 27.5 60.0 67.5 87.5 170.0
Min. 1st Qu. Median Mean 3rd Qu. Max.
50.0 105.0 160.0 146.2 185.0 220.0
\(E[Y|A]= \theta_0 + \theta_1A\)
[1] 216.89
[1] 197.1269
Qual o certo?
[1] "216.89 IC = 172.1 - 261.6)"
[1] "197.13 IC = 142.8 - 251.5)"